1. Field of the Invention
This invention pertains generally to computed tomography imaging, and more particularly to automated detection and measurement of lung nodules in medical images.
2. Description of Related Art
Computed Tomography (CT) imaging has been used for in vivo assessment of the location, extent, and progression of lung disease in patients. The ability to perform these analyses routinely and reliably in large patient cohorts is important to enable deployment of the methods in clinical trials and practice. However, the role of diagnostic imaging has generally been limited to visual inspection in clinical practice.
For analysis to be feasible in clinical practice, reliable automation is needed based on the size of the data sets (>400 cross-sectional images for isotropic voxel spacing). Lung cancer is the leading cause of death due to cancer. Imaging is used for detection, diagnosis, measurement, and follow-up of lung nodules.
Nodule detection is one of the more challenging visual detection tasks in medical imaging. Nodules may be difficult to detect visually on images because of low contrast, small size, or location of the nodule within an area of complicated anatomy such as the hilum. Reader fatigue, distraction, and satisfaction of search from the observation of unrelated pathology are other recognized causes of missed nodules. Thinner slices and overlapping reconstruction intervals improve the longitudinal resolution, but require large data sets (700 or more cross-sectional images) to be generated, contributing to the difficulty of interpretation and potential for missed nodules.
It has been shown that automated computer detection of lung nodules can assist a reader in more accurate and consistent detection of lung nodules [Brown 2005].
There have been numerous computer-aided detection (CAD) systems developed for lung nodules in computed tomography (CT) images [Girvin 2008]. However, CAD is not in widespread clinical use because of an inability to limit false positive detections, e.g., normal anatomy such as blood vessel or airway branches that are incorrectly detected by CAD as nodules. These false positives not only take time to rule out, but some studies suggest that radiologists can incorrectly accept false positives, which in practice would lead to unnecessary workups.
Also, most previous methods have tended to focus on solid nodules which appear brighter in images and are thus easier to detect (e.g., using a threshold above −300 HU). If a method attempts to detect faint ground glass nodules (with intensity of around −700 HU) they typically generate too many false positives to be practical.